Immune Gene Signatures in Cancer

ABSTRACT

This invention relates to methods for selecting a treatment, treating, and predicting survival time in subjects with cancer, such as colorectal cancer, based on tumor expression levels of chemokines, cytotoxic genes, and/or dendritic cell genes.

CLAIM OF PRIORITY

This application is a continuation of U.S. application Ser. No.16/029,499, filed on Jul. 6, 2018, which is a continuation of U.S.application Ser. No. 15/171,713, filed on Jun. 2, 2016, now U.S. Pat.No. 10,041,129, which is a continuation of U.S. application Ser. No.13/575,354, filed on Jul. 26, 2012, now U.S. Pat. No. 9,404,926, whichis a U.S. National Phase Application of International Patent ApplicationNo. PCT/US2011/022845, filed on Jan. 28, 2011, which claims the benefitof U.S. Patent Application No. 61/299,798, filed on Jan. 29, 2010. Theentire contents of the foregoing are hereby incorporated by reference.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Grant No. R01CA148995-01 awarded by the National Institutes of Health. The Governmenthas certain rights in the invention.

TECHNICAL FIELD

This invention relates to methods for identifying tumors associated withimmune cell infiltration, and for making a prognosis in subjects withcancer, such as colorectal cancer.

BACKGROUND

Tumor-induced, host immune response has been described in breast (2-5),lung (6, 7), ovarian (8, 9), and CRC (10, 11) among other solid tumortypes. This response may include fibrosis, lymphocytic or neutrophilicinfiltration, and other reactive changes within the tumor and/or in thesurrounding tissue.

SUMMARY

The present invention is based, at least in part, on the discovery ofgene signatures that predict the presence of infiltrating immune cells.Expression levels of these genes can be used to optimize or selecttreatment and predict survival in subjects with tumors.

Thus, in a first aspect, the invention provides methods for predictingsurvival time for a subject who has a tumor. The methods includeobtaining cells from the tumor; determining one or more of:

-   -   (i) gene expression levels of chemokines CCL2, CCL3, CCL4, CCL5,        CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13 in        the tumor cells;    -   (ii) gene expression levels of cytotoxic cell genes cathepsin H        (CTSH), CTSC, CTSD, CTSE, CTSO, CTSS, CTSZ, granzyme A (GZMA),        GZMB, GZMH, GZMK, Fcgamma receptor (FcgammaR) type IIa (FCGR2A),        FCGR2B, FCGR2C, Fcgamma receptor (FcgammaR) type IIIa (FCGR3A),        FCGR3B in the tumor cells; or    -   (iii) gene expression levels of dendritic cell genes S 100PBP,        S100A10, S100A6, S100A7L1, S100G, S100A1, S100A7, S100A14,        S100A16, S100A2, S100A11, S100P, S100Z, S100A3, S100A13,        S100A12, S100B, S100A4, S100A9, S100A8, and CD209 in the tumor        cells;        comparing the tumor gene expression levels to reference gene        expression levels; and predicting longer survival time if tumor        gene expression levels are above the reference gene expression        levels, or predicting shorter survival time if tumor gene        expression levels are below the reference gene expression        levels.

In another aspect, the invention provides methods for monitoring animmunotherapy in a subject who has a tumor. The methods includeobtaining cells from the tumor; determining first gene expression levelsof one or more of:

-   -   (i) gene expression levels of chemokines CCL2, CCL3, CCL4, CCL5,        CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13 in        the tumor cells;    -   (ii) gene expression levels of cytotoxic cell genes cathepsin H        (CTSH), CTSC, CTSD, CTSE, CTSO, CTSS, CTSZ, granzyme A (GZMA),        GZMB, GZMH, GZMK, Fcgamma receptor (FcgammaR) type IIa (FCGR2A),        FCGR2B, FCGR2C, Fcgamma receptor (FcgammaR) type IIIa (FCGR3A),        FCGR3B in the tumor cells; or    -   (iii) gene expression levels of dendritic cell genes S 100PBP,        S100A10, S100A6, S100A7L1, S100G, S100A1, S100A7, S100A14,        S100A16, S100A2, S100A11, S100P, S100Z, S100A3, S100A13,        S100A12, S100B, S100A4, S100A9, S100A8, and CD209 in the tumor        cells;        administering one or more doses of an immunotherapy to the        subject; determining second gene expression levels of the same        genes in the tumor cells; and comparing the first and second        gene expression levels, wherein second gene expression levels        that are higher than the first gene expression levels indicate        that the treatment is effective, and second gene expression        levels that are the same as or lower that the first gene        expression levels indicate that the treatment is not effective.

In another aspect, the invention provides methods for treating a subjectwho has a tumor. The methods include obtaining cells from the tumor;determining expression levels of one or more of:

-   -   (i) gene expression levels of chemokines CCL2, CCL3, CCL4, CCL5,        CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13 in        the tumor cells;    -   (ii) gene expression levels of cytotoxic cell genes cathepsin H        (CTSH), CTSC, CTSD, CTSE, CTSO, CTSS, CTSZ, granzyme A (GZMA),        GZMB, GZMH, GZMK, Fcgamma receptor (FcgammaR) type IIa (FCGR2A),        FCGR2B, FCGR2C, Fcgamma receptor (FcgammaR) type IIIa (FCGR3A),        FCGR3B in the tumor cells; or    -   (iii) gene expression levels of dendritic cell genes S100PBP,        S100A10, S100A6, S100A7L1, S100G, S100A1, S100A7, S100A14,        S100A16, S100A2, S100A11, S100P, S100Z, S100A3, S100A13,        S100A12, S100B, S100A4, S100A9, S100A8, and CD209 in the tumor        cells;        comparing the tumor gene expression levels to reference gene        expression levels; and selecting for the subject a treatment        comprising an immunotherapy if tumor gene expression levels are        above the reference gene expression levels, or selecting for the        subject a treatment not comprising an immunotherapy if tumor        gene expression levels are below the reference gene expression        levels.

In a further aspect, the invention provides methods for selecting atreatment for a subject who has a tumor. The methods include obtainingcells from the tumor; determining one or more of:

-   -   (i) gene expression levels of chemokines CCL2, CCL3, CCL4, CCL5,        CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13 in        the tumor cells;    -   (ii) gene expression levels of cytotoxic cell genes cathepsin H        (CTSH), CTSC, CTSD, CTSE, CTSO, CTSS, CTSZ, granzyme A (GZMA),        GZMB, GZMH, GZMK, Fcgamma receptor (FcgammaR) type IIa (FCGR2A),        FCGR2B, FCGR2C, Fcgamma receptor (FcgammaR) type IIIa (FCGR3A),        FCGR3B in the tumor cells; and    -   (iii) gene expression levels of dendritic cell genes S100PBP,        S100A10, S100A6, S100A7L1, S100G, S100A1, S100A7, S100A14,        S100A16, S100A2, S100A11, S100P, S100Z, S100A3, S100A13,        S100A12, S100B, S100A4, S100A9, S100A8, and CD209 in the tumor        cells;        comparing the tumor gene expression levels to reference gene        expression levels; and selecting for the subject a treatment        comprising an immunotherapy if tumor gene expression levels are        above the reference gene expression levels, or selecting for the        subject a treatment not comprising an immunotherapy if tumor        gene expression levels are below the reference gene expression        levels.

In some embodiments of the methods described herein, determining geneexpression levels comprises determining protein levels. In someembodiments of the methods described herein, determining gene expressionlevels comprises determining mRNA levels.

In some embodiments of the methods described herein, the methods includedetermining chemokine gene expression levels. In some embodiments of themethods described herein, the methods include determining cytotoxic cellgene expression levels. In some embodiments of the methods describedherein, the methods include determining dendritic cell gene expressionlevels. In some embodiments of the methods described herein, the methodsinclude determining chemokine gene expression levels and cytotoxic cellgene expression levels. In some embodiments of the methods describedherein, the methods include determining cytotoxic cell gene expressionlevels and dendritic cell gene expression levels. In some embodiments ofthe methods described herein, the methods include determining chemokinegene expression levels and dendritic cell gene expression levels. Insome embodiments of the methods described herein, the methods includedetermining chemokine gene expression levels, cytotoxic cell geneexpression levels, and dendritic cell gene expression levels.

In some embodiments of the methods described herein, the longer survivaltime is 2 years or more, and the shorter survival time is less than 2years.

In some embodiments of the methods described herein, the methods furtherinclude communicating predicted survival time to the subject or a healthcare provider. In some embodiments of the methods described herein, themethods further include communicating information regarding theeffectiveness of a treatment to the subject or a health care provider.In some embodiments of the methods described herein, the methods furtherinclude communicating information regarding treatment or selection of atreatment to the subject or a health care provider.

In some embodiments of the methods described herein, immunotherapycomprises administering to the subject dendritic cells or peptides withadjuvant, a DNA-based vaccine, cytokines, cyclophosphamide,anti-interleukin-2R immunotoxin, or an anti-cancer antibody. In someembodiments, the antibody is anti-CD137, anti-PD1, or anti-CTLA-4. Insome embodiments, the immunotherapy comprises administering to thesubject tumor-pulsed dendritic cells.

In some embodiments of the methods described herein, the subject is ahuman.

In some embodiments of the methods described herein, the tumor is asolid tumor.

In some embodiments of the methods described herein, the tumor iscolorectal cancer.

A “subject” as described herein can be any subject having aproliferative disorder. For example, the subject can be any mammal, suchas a human, including a human cancer patient. Exemplary nonhuman mammalsinclude a nonhuman primate (such as a monkey or ape), mouse, rat, goat,cattle, pig, horse, sheep, cat, and dog.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Methods and materials aredescribed herein for use in the present invention; other, suitablemethods and materials known in the art can also be used. The materials,methods, and examples are illustrative only and not intended to belimiting. All publications, patent applications, patents, sequences,database entries, and other references mentioned herein are incorporatedby reference in their entirety. In case of conflict, the presentspecification, including definitions, will control.

Other features and advantages of the invention will be apparent from thefollowing detailed description and figures, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1A is a bar graph illustrating the observed range of the immuneresponse as represented by the metagene #1 score. The barplot shows CRCsthat have 11 highest and 10 lowest values of the mean score of selectedimmune metagenes grouped by whether they had the highest 11 or lowest 10scores and sorted by the mean metagene score.

FIG. 1B is a bar graph illustrating of the relationship between patientoverall survival and the immune response as quantified by the score ofmetagene #1 on selected CRCs with known status of ectopic lymphnode-like structures confirmed by immunohistochemistry. The barplot ofCRCs without lymphoid structures and CRCs with lymphoid structuressegregated into two groups: those with overall survival time less than 2years (shown on the left), and those with overall survival time greaterthan 2 years (shown on the right). Score for metagene #1 is plotted onthe Y-axis.

FIGS. 2A-J are a set of images showing the results of H & E staining andimmunohistochemistry analysis of primary CRCs. All 10 of the lowest genesignature-scored CRCs revealed a lightly dispersed or absent lymphocyticperitumoral host response, and low to no appreciable expression of Bcell (i.e., CD20; 2A) and T cell (i.e., CD3; 2B) markers. All 11 of thehighest gene signature-scored CRCs, revealed a marked peritumorallymphocytic host response organized as ectopic lymph node-likestructures by hematoxylin and eosin staining (arrows; 2C) and byimmunohistochemistry (2D-I). CD20⁺ (2E, F) and CD79a⁺ (2G) B cells andCD21⁺ follicular dendritic cells (J) are concentrated in the center offollicles with CD3⁺ (2D, H, I) T cells appearing in the parafollicularcortex or marginal zones, with some dispersion into the follicles. Insome cases, a fibrous stroma was observed to encapsulate a follicle(2I). In panels H and I, T=T cells, B=B cells, and S=stroma. In panel J,T=tumor; the follicle (arrow) is at the front edge of the invadingcolonic adenocarcinoma.

FIGS. 3A-B show that chemokines are upregulated in tumors withlymphocyte involvement. Hierarchical clustering of tumors with andwithout lymphoid structures done on selected set of known chemokines.For each gene, a single representative probe set with the highestdynamic range across all profiled samples was picked up from all probesets that mapped to a given gene symbol. Genes are clustered usingPearson correlation distance metric, tumors are clustered usingEuclidean distance metric. Ward linkage was applied in both cases, forclustering tumors and genes. FIG. 3A is a heatmap showing mean-centeredintensities (averaged within each probe set across all tumors shown).FIG. 3B is a bar graph displaying chemokine score: the mean value ofchemokines as averaged across all probe sets shown in 3A.

FIG. 4A is a heat map showing 12 chemokines that correlate with metagene#1. The heatmap of 326 colorectal tumors and 12 genes comprisingmetagene #1 sorted by the metagene score (mean of probesets that map toa given 12-gene gene set). The 11 samples with the highest metagenescore are at the top of the figure, and 10 samples with the lowestmetagene score are at the bottom of this figure.

FIG. 4B is a bar graph illustrating the relationship between patientoverall survival and the immune response as quantified by the score ofchemokine genes on selected CRCs with known status of ectopic lymphnode-like structures confirmed by immunohistochemistry. The barplot ofCRCs without lymphoid structures and CRCs with lymphoid structuressegregated into two groups: those with overall survival time less than 2years (shown on the left), and those with overall survival time greaterthan 2 years (shown on the right). Scores for the chemokine genes areplotted on the Y-axis.

FIGS. 5A-B show that cytotoxic genes are upregulated in tumors withectopic lymph node-like structures. Hierarchical clustering of tumorswith and without lymphoid structures were performed on a selected set ofknown cytotoxic genes. For each gene, a single representative probe setwith the highest dynamic range across all profiled samples was picked upfrom all probe sets that mapped to a given gene symbol. Genes areclustered using Pearson correlation distance metric; tumors areclustered using Euclidean distance metric. Ward linkage was applied inboth cases, for clustering tumors and genes. FIG. 5A is a heatmapshowing mean-centered intensities (averaged within each probe set acrossall tumors shown). FIG. 5B is a bar plot displaying the mean value ofcytotoxic genes as averaged across all probe sets.

FIGS. 6A-B shows that dendritic cell marker genes are upregulated inCRCs with ectopic lymph node involvement. Hierarchical clustering oftumors with and without lymphoid structures was done on a selected setof known dendritic cell marker genes. For each gene, a singlerepresentative probe set with the highest dynamic range across allprofiled samples was picked up from all probe sets that mapped to agiven gene symbol. Genes are clustered using Pearson correlationdistance metric, tumors are sorted by the dendritic genes score (shownin 6B) computed as the mean value for each tumor across all genes shownon the figure. Ward linkage was applied for clustering genes. FIG. 6A isa heatmap showing mean-centered intensities (averaged within each probeset across all tumors shown). FIG. 6B is a bar plot displaying the meanvalue of dendritic cell marker genes as averaged across all probe sets.

FIGS. 7A-B are bar graphs illustrating the relationship between patientoverall survival and the immune response as quantified by the score ofcytotoxic cell—(7A) and dendritic cell—(7B) related genes on selectedCRCs with known status of ectopic lymph node-like structures confirmedby immunohistochemistry. The barplot of CRCs without lymphoid structuresand CRCs with lymphoid structures segregated into two groups: those withoverall survival time less than 2 years (shown on the left), and thosewith overall survival time greater than 2 years (shown on the right).Scores for cytotoxic cell- and dendritic cell-related genes are plottedon the Y-axis.

DETAILED DESCRIPTION

It has been shown that some growing, human solid tumors are infiltratedby immune cells. Data characterizing the nature of this host immuneresponse in a wide variety of distinct tumor types have been publishedin the recent literature (2-9), including primary CRCs (14). As shownherein, profiling gene signatures predicts the presence of infiltratingimmune cells, and expression levels of these genes can be used to assigna prognosis and select or optimize treatment in subjects with tumors.

Methods of Assigning a Prognosis or Predicting Survival

The methods can be used to monitor a treatment (e.g., an immunotherapy),or to select a treatment, e.g., to select a treatment regime includingan immunotherapy for a subject. In addition, the methods describedherein can be used for, e.g., to assist in, assigning a prognosis orpredicting survival in a subject who has a tumor, e.g., a solid tumor.

As used herein, the term “cancer” refers to cells having the capacityfor autonomous growth, i.e., an abnormal state or conditioncharacterized by rapidly proliferating cell growth. Hyperproliferativeand neoplastic disease states may be categorized as pathologic, i.e.,characterizing or constituting a disease state, or may be categorized asnon-pathologic, i.e., a deviation from normal but not associated with adisease state. In general, a cancer will be associated with the presenceof one or more tumors, i.e., abnormal cell masses. The term “tumor” ismeant to include all types of cancerous growths or oncogenic processes,metastatic tissues or malignantly transformed cells, tissues, or organs,irrespective of histopathologic type or stage of invasiveness.“Pathologic hyperproliferative” cells occur in disease statescharacterized by malignant tumor growth. In general, the methodsdescribed herein can be practiced on subjects with solid tumors.

Tumors include malignancies of the various organ systems, such asaffecting lung, breast, thyroid, lymphoid, gastrointestinal, andgenito-urinary tract, as well as adenocarcinomas which includemalignancies such as most colon cancers, renal-cell carcinoma, prostatecancer and/or testicular tumors, non-small cell carcinoma of the lung,cancer of the small intestine and cancer of the esophagus. The term“carcinoma” is art recognized and refers to malignancies of epithelialor endocrine tissues including respiratory system carcinomas,gastrointestinal system carcinomas, genitourinary system carcinomas,testicular carcinomas, breast carcinomas, prostatic carcinomas,endocrine system carcinomas, and melanomas. In some embodiments, thedisease is renal carcinoma or melanoma. Exemplary carcinomas includethose forming from tissue of the cervix, lung, prostate, breast, headand neck, colon and ovary. The term also includes carcinosarcomas, e.g.,which include malignant tumors composed of carcinomatous and sarcomatoustissues. An “adenocarcinoma” refers to a carcinoma derived fromglandular tissue or in which the tumor cells form recognizable glandularstructures. The term “sarcoma” is art recognized and refers to malignanttumors of mesenchymal derivation.

In some embodiments, cancers evaluated by the methods described hereininclude those that are particularly immunogenic, e.g., neuroblastoma,melanoma, and renal cell cancer.

In some embodiments, cancers evaluated by the methods described hereininclude epithelial cancers, such as a lung cancer (e.g., non-small-celllung cancer (NSCLC)), breast cancer, colorectal cancer, head and neckcancer, or ovarian cancer. Epithelial malignancies are cancers thataffect epithelial tissues.

Lymphoid Structures in Solid Tumors

Lymphoid structures have been described in solid tumors. As examples,Coronella-Wood et al. (2, 3) have described breast tumor-infiltratinglymphocytes composed of B cell aggregates containing interdigitatingCD21+ follicular dendritic cells. The presence of ectopic, organizedlymphoid tissue has also been reported in ovarian (8, 9, 19); colon (20,21); and lung tumors (6, 7), which has mostly focused on the presence ofdendritic cell subpopulations, the level of which predicted betterprognosis in some tumor types (7, 8, 22). A similar correlation wasrecently reported for patients with CRC (10).

Colorectal Adenocarcinoma (CRC)

In some embodiments, the methods herein can be used to select treatmentor predict survival in a subject who has colorectal adenocarcinoma(CRC). CRC is one of the most common malignancies, accounting forapproximately 15% of all cancer-related deaths in the U.S. Theprevalence of CRC increases with age, the largest number of tumorsoccurring during the sixth decade. The expected annual incidence of thistumor has risen over the last decade and 149,000 new cases wereestimated in 2009 (1). If not diagnosed and treated early, this tumorspreads through the entire bowel wall, extends to adjacent organs, andeventually metastasizes to regional lymph nodes and distant sites. Themajority of deaths from CRC occur in patients with metastatic late stagetumors, which are incurable most of the time.

CRC is known to elicit an inflammatory immune reaction composed of acuteand/or chronic inflammatory cells, including lymphocytes, infiltratingthe tumor as well as the surrounding colonic wall. The lymphocyticcomponent of this response has been shown to include someantigen-specific T cells originating without prior immunotherapy (12,13). As described herein, immune gene-related signatures predict thepresence of unique histologic features of lymphoid cell infiltrates incolorectal carcinoma (CRCs) that correlate with clinical parameters.

Assays, References, and Samples

The methods described herein include determining levels of selectedimmune-related genes, i.e., chemokines, cytotoxic cell genes, and/ordendritic cell genes. In some embodiments, all of the genes listed inthe tables below are evaluated. In some embodiments, two, three, four,five, six, seven, eight, nine, ten, eleven, twelve, or more of thelisted genes are evaluated. Although the terminology “genes” is usedherein, in some embodiments, the methods include detecting levels of theproteins encoded by the listed genes. In some embodiments, the methodsinclude detecting transcript (mRNA) levels.

Chemokines

Chemokines are secreted proteins involved in immunoregulatory andinflammatory processes. The chemokines used in the present methods areas follows:

Chemokines GenBank GenBank Gene Acc. No.: Acc. No.: Symbol Gene NameNucleic Acid Protein CCL2 chemokine (C-C motif) ligand 2 NM_002982.3NP_002973.1 CCL3 chemokine (C-C motif) ligand 3 NM_002983.2 NP_002974.1CCL4 chemokine (C-C motif) ligand 4 NM_002984.2 NP_002975.1 CCL5chemokine (C-C motif) ligand 5 NM_002985.2 NP_002976.2 CCL8 chemokine(C-C motif) ligand 8 NM_005623.2 NP_005614.2 CCL18 chemokine (C-C motif)ligand 18 NM_002988.2 NP_002979.1 (pulmonary and activation-regulated)CCL19 chemokine (C-C motif) ligand 19 NM_006274.2 NP_006265.1 CCL21chemokine (C-C motif) ligand 21 NM_002989.2 NP_002980.1 CXCL9 chemokine(C-X-C motif) ligand 9 NM_002416.1 NP_002407.1 CXCL10 chemokine (C-X-Cmotif) ligand 10 NM_001565.2 NP_001556.2 CXCL11 chemokine (C-X-C motif)ligand 11 NM_005409.4 NP_005400.1 CXCL13 chemokine (C-X-C motif) ligand13 NM_006419.2 NP_006410.1

Cytotoxic Cell Genes

The cytotoxic cell genes evaluated in the methods described herein arethose expressed by cytotoxic cells involved in the immune response,e.g., lysosomal/proteolytic enzymes (cathepsins), granzymes, and FcgR2s.

Cytotoxic Genes GenBank GenBank Gene Acc. No.: Acc. No.: Symbol GeneName Nucleic Acid Protein CTSH Cathepsin H NM_004390.3 NP_004381.2 CTSCCathepsin C NM_001814.4 NP_001805.3 (Dipeptidyl peptidase 1) CTSDCathepsin D NM_001909.3 NP_001900.1 CTSE Cathepsin E NM_001910.2;NP_001901.1; NM_148964.1 NP_683865.1 CTSO Cathepsin O NM_001334.2NP_001325.1 CTSS Cathepsin S NM_004079.3 NP_004070.3 CTSZ Cathepsin ZNM_001336.3 NP_0013272 GZMA Granzyme A NM_006141.3 NP_006135.1 GZMBGranzyme B NM_004131.4 NP_004122.2 GZMH Granzyme H NM_033423.3NP_219491.1 GZMK Granzyme K NM_002104.2 NP_002095.1 FCGR2A Fcgammareceptor NM_001136219.1.; NP_001129691.1; (FcgammaR) type IIaNM_021642.3 NP_067674.2 FCGR2B Fcgamma receptor NM_001002273.2NP_001002273.1 (FcgammaR) type IIb NM_001002274.2 NP_001002274.1NM_001002275.2 NP_001002275.1 NM_001190828.1 NP_001177757.1 NM_004001.4.NP_003992.3 FCGR2C Fcgamma receptor NM_201563.4 NP_963857.3 (FcgammaR)type IIc FCGR3A Fcgamma receptor NM_000569.6 NP_000560.5 (FcgammaR) typeIIIa NM_001127592.1 NP_001121064.1 NM_001127593.1 NP_001121065.1NM_001127595.1 NP_001121067.1 NM_001127596.1. NP_001121068.1 FCGR3BFcgamma receptor NM_000570.3 NP_000561.3. (FcgammaR) type IIIb

Dendritic Cell Genes

Most of the dendritic cell genes evaluated in the methods describedherein belong to the S100 family of proteins, and contain two EF-handcalcium-binding motifs. S100 proteins are involved in the regulation ofa number of cellular processes such as cell cycle progression anddifferentiation. CD209 is a pathogen-recognition receptor expressed onthe surface of immature dendritic cells; it is believed to be involvedin initiation of the primary immune response.

Dendritic Cell Genes GenBank GenBank Gene Acc. No.: Acc. No.: SymbolGene Name Nucleic Acid Protein S100PBP S100P Binding Protein NM_022753.2NP_073590.2 S100A10 S100 calcium-binding protein A10 NM_002966.2NP_002957.1 S100A6 S100 calcium-binding protein A6 NM_014624.3NP_055439.1 S100A7L1 S100 calcium-binding protein A7-like 1 NM_176823.3NP_789793.1 S100G S100 calcium-binding protein G NM_0040572 NP_004048.1S100A1 S100 calcium-binding protein A1 NM_006271.1 NP_006262.1 S100A7S100 calcium-binding protein A7 NM_002963.3 NP_002954.2 S100A14 S100calcium-binding protein A14 NM_020672.1 NP_065723.1 S100A16 S100calcium-binding protein A16 NM_080388.1 NP_525127.1 S100A2 S100calcium-binding protein A2 NM_005978.3. NP_005969.1 S100A11 S100calcium-binding protein A11 NM_005620.1 NP_005611.1 S100P S100calcium-binding protein P NM_005980.2 NP_005971.1. S100Z S100calcium-binding protein Z NM_130772.3 NP_570128.2 S100A3 S100calcium-binding protein A3 NM_002960.1 NP_002951.1 S100A13 S100calcium-binding protein A13 NM_001024210.1 NP_001019381.1 NM_001024211.1NP_001019382.1 NM_001024212.1 NP_001019383.1 NM_001024213.1NP_001019384.1 NM_005979.2 NP_005970.1 S100A12 S100 calcium-bindingprotein A12 NM_005621.1 NP_005612.1 S100B S100 calcium-binding protein BNM_006272.2 NP_006263.1 S100A4 S100 calcium-binding protein A4NM_002961.2; NP_002952.1; NM_019554.2 NP_062427.1 S100A9 S100calcium-binding protein A9 NM_002965.3 NP_002956.1 S100A8 S100calcium-binding protein A8 NM_002964.3 NP_002955.2 CD209 CD209 antigenNM_001144893.1 NP_001138365.1 NM_001144894.1 NP_001138366.1NM_001144895.1 NP_001138367.1 NM_001144896.1 NP_001138368.1NM_001144897.1 NP_001138369.1 NM_021155.3 NP_066978.1

In some embodiments, the methods include assaying the presence or levelsof immune-related mRNA or proteins in the sample. The presence and/orlevel of a protein can be evaluated using methods known in the art,e.g., using quantitative immunoassay methods. The presence and/or levelof an mRNA can be evaluated using methods known in the art, e.g.,Northern blotting or quantitative PCR methods, e.g., RT-PCR. In someembodiments, high throughput methods, e.g., protein or gene chips as areknown in the art (see, e.g., Ch. 12, Genomics, in Griffiths et al., Eds.Modern genetic Analysis, 1999, W. H. Freeman and Company; Ekins and Chu,Trends in Biotechnology, 1999, 17:217-218; MacBeath and Schreiber,Science 2000, 289(5485):1760-1763; Simpson, Proteins and Proteomics: ALaboratory Manual, Cold Spring Harbor Laboratory Press; 2002; Hardiman,Microarrays Methods and Applications: Nuts &Bolts, DNA Press, 2003), canbe used to detect the presence and/or level of chemokine proteins asdescribed herein.

In some embodiments, the methods include assaying levels of one or morecontrol genes or proteins, and comparing the level of expression of theimmune-related genes or proteins to the level of the control genes orproteins, to normalize the levels of the immune-related genes orproteins. Suitable endogenous control genes includes a gene whoseexpression level should not differ between samples, such as ahousekeeping or maintenance gene, e.g., 18S ribosomal RNA; beta Actin;Glyceraldehyde-3-phosphate dehydrogenase; Phosphoglycerate kinase 1;Peptidylprolyl isomerase A (cyclophilin A); Ribosomal protein L13a;large Ribosomal protein P0; Beta-2-microglobulin; Tyrosine3-monooxygenase/tryptophan 5-monooxygenase activation protein, zetapolypeptide; Succinate dehydrogenase; Transferrin receptor (p90, CD71);Aminolevulinate, delta-, synthase 1; Glucuronidase, beta;Hydroxymethyl-bilane synthase; Hypoxanthine phosphoribosyltransferase 1;TATA box binding protein; and/or Tubulin, beta polypeptide.

Generally speaking, the methods described herein can be performed oncells from a tumor. The cells can be obtained by known methods, e.g.,during a biopsy (such as a core needle biopsy), or during a surgicalprocedure to remove all or part of the tumor. The cells can be usedfresh, frozen, fixed, and/or preserved, so long as the mRNA or proteinthat is to be assayed is maintained in a sufficiently intact state toallow accurate analysis.

In some embodiments of the methods described herein, the levels of theimmune-related genes in the tumor sample can be compared individually tolevels in a reference. The reference levels can represent levels in asubject who has a good prognosis, or a long predicted survival time(e.g., 2 years or more). Alternatively, reference levels can representlevels in a subject who has a poor prognosis, or a shorter predictedsurvival time (e.g., less than 2 years). In some embodiments, thereference levels represent a threshold, and a level in the tumor that isabove the threshold reference level indicates that the subject has agood prognosis, or a long predicted survival time (e.g., 2 years ormore), and levels below the threshold reference level indicates that thesubject has a poor prognosis, or a shorter predicted survival time(e.g., less than 2 years).

In some embodiments, the reference levels can represent levels in asubject who has lymphoid like structures present in the tumor, or ispredicted to respond to immunotherapy. Alternatively, reference levelscan represent levels in a subject who lacks tumor lymphoid structures,or is predicted to have no or a poor response to immunotherapy. In someembodiments, the reference levels represent a threshold, and a level inthe tumor that is above the threshold reference level indicates that thesubject has tumor lymphoid structures, or is predicted to respond toimmunotherapy, and levels below the threshold reference level indicatesthat the subject lacks lymphoid structures and is predicted to have noor poor response to immunotherapy. In subjects who are predicted to havetumor lymphoid structures, or who are predicted to respond toimmunotherapy, the methods can further include administering animmunotherapy for those subjects, or selecting or recommending atreatment including an immunotherapy for those subjects.

In some embodiments of the methods described herein, values representingthe levels of the immune-related genes can be summed to produce a “tumorimmune-related gene score” that can be compared to a referenceimmune-related gene score, wherein a tumor immune-related gene scorethat is above the reference immune-related gene score indicates that thesubject has a long predicted survival time (e.g., 2 years or more) or ispredicted to have a positive response to immunotherapy, and animmune-related gene score below the reference score indicates that thesubject has a shorter predicted survival time (e.g., less than 2 years),or is predicted to have no or a poor response to immunotherapy.

For example, in some embodiments, the expression levels of each of theevaluated genes can be assigned a value (e.g., a value that representsthe expression level of the gene, e.g., normalized to an endogenouscontrol gene as described herein). That value (optionally weighted toincrease or decrease its effect on the final score) can be summed toproduce an immune-related gene score. One of skill in the art couldoptimize such a method to determine an optimal algorithm for determiningan immune-related gene score.

The methods described herein can include determining levels (or scores)for all of the 12 chemokines, 20 dendritic cell genes, and 16 cytotoxiccell genes; or for the 12 chemokines and 20 dendritic cell genes; or forthe 20 dendritic cell genes, and 16 cytotoxic cell genes; or for the 12chemokines, and 16 cytotoxic cell genes, or for any of the gene setsalone. In some embodiments all of the genes in each set are evaluated,but in some embodiments a subset of one or all of the sets is evaluated.

One of skill in the art will appreciate that references can bedetermined using known epidemiological and statistical methods, e.g., bydetermining an immune-related gene score, or immune-related gene proteinor mRNA levels, in tumors from an appropriate cohort of subjects, e.g.,subjects with the same type of cancer as the test subject and a knownprognosis (e.g., good or poor) or predicted survival time (e.g., lessthan 2 years, or 2 years or more).

In some embodiments, the methods can be used to monitor the efficacy ofa treatment, e.g., an immunotherapy, e.g., methods comprisingadministering to the subject therapies that promote anti-cancerimmunity, including administering one or more of: dendritic cells orpeptides with adjuvant, DNA-based vaccines, cytokines (e.g., IL-2),cyclophosphamide, anti-interleukin-2R immunotoxins, and/or antibodiessuch as anti-CD137, anti-PD1, or anti-CTLA-4; see, e.g., Kruger et al.,“Immune based therapies in cancer,” Histol Histopathol. 2007 June;22(6):687-96; Eggermont et al., “Anti-CTLA-4 antibody adjuvant therapyin melanoma,” Semin Oncol. 2010 October; 37(5):455-9; Klinke D J 2nd, “Amultiscale systems perspective on cancer, immunotherapy, andInterleukin-12,” Mol Cancer. 2010 Sep. 15; 9:242; Alexandrescu et al.,“Immunotherapy for melanoma: current status and perspectives,” JImmunother. 2010 July-August; 33(6):570-90; Moschella et al.,“Combination strategies for enhancing the efficacy of immunotherapy incancer patients,” Ann N Y Acad Sci. 2010 April; 1194:169-78; Ganesan andBakhshi, “Systemic therapy for melanoma,” Natl Med J India. 2010January-February; 23(1):21-7; Golovina and Vonderheide, “Regulatory Tcells: overcoming suppression of T-cell immunity,” Cancer J. 2010July-August; 16(4):342-7. In some embodiments, the methods includeadministering a composition comprising tumor-pulsed dendritic cells,e.g., as described in WO2009/114547 and references cited therein. Themethods include determining levels of the immune-related genes in asample, then administering one or more doses of the treatment, thendetermining levels of the immune-related genes to determine whether thetreatment has increase immune infiltration of the tumor. An increase inimmune-related gene levels (or immune-related gene score, if calculated)indicates that the treatment was effective.

EXAMPLES

The invention is further described in the following examples, which donot limit the scope of the invention described in the claims.

Statistical analysis for the following examples was performed asfollows. On the figure legends, the term “significance” denoted p-valueby Fisher exact test. Throughout the paper, for each gene, a singleprobe set with highest standard deviation across all samples, wasselected among multiple probesets available on the array that mapped tothe same gene symbol. Significance of linkages of gene profiles topatient survival was analyzed statistically by both ANOVA and WilcoxonRank-Sum tests.

Example 1. Immune Gene Profiling

Several metagenes—tightly correlated sets of genes biologically relatedto inflammation and immune response—were identified. Metagene analysiswas performed on samples from the Moffitt Cancer Center (MCC) CRC500tumor bank and CRCs were sorted by low versus high scores.

Selection of human tissues was performed as follows. The MCC CRC500 geneprofiling database was interrogated for the presence of genesbiologically related to inflammation and immune response. The 11 CRCswith the highest expression of these genes (2 of the CRCs, denotedT2157A1 and T2157A3, are separate samples obtained from the samepatient) were selected and compared with the 10 CRCs with the lowest orabsent expression of the same genes. The histologic slides correspondingto these cases, and prepared from the mirror-image of the portion oftumor submitted for the mRNA microarray analysis, were retrieved fromthe MCC Anatomic Pathology Division's repository. All of the specimenswere preserved in 10% buffered formalin prior to embedding in paraffin.The slides were reviewed to assess the presence of microscopicallyevident host immune response. The final pathology report for each casewas also reviewed and the pathologic data were collected.

The tumors were staged according to both Dukes and TNM systems. Alltumors occurred in the absence of genetic cancer syndromes such as humannon-polyposis colon cancer syndrome (HNPCC), familial adenomatouspolyposis syndrome (FAP), among others; also cancers arising in thebackground of ulcerative colitis or Crohn's disease were excluded fromthe study. Linked, annotated clinical follow-up data (e.g., survival)and treatments received were also available in the database.

mRNA microarray analysis was performed as follows. Twelve normalcolorectal mucosa samples, 9 normal liver samples, and 326 colorectaladenocarcinoma tumor specimens (19 annotated as metastasis, 265 asprimary, and 42 as unknown) from human patients were arrayed onAffymetrix HG-U133+ GeneChip microarrays (denoted MCC CRC500). For thecurrent study, this existing MCC CRC500 database was interrogated andthe data were processed using RMA normalization algorithm as implementedin Affymetrix Power Tools software package (APT; which are a set ofcross-platform command line programs that implement algorithms foranalyzing and working with Affymetrix GeneChip arrays) using defaultsettings. Obtained probe set intensities were then converted to log 10.Probes were selected for heatmaps by starting with all gene symbols froma given family and then reducing to probes that showed a desiredcorrelation pattern.

Three hundred and twenty six “gene chipped” CRCs were evaluated fromthis tumor bank. The chip contained 20,155 unique genes. About 50separate metagene groupings were derived. Among them a metagene(Metagene #1) with overwhelming enrichment for immune- andinflammation-related genes was identified. It is also the largestmetagene in terms of number of genes (comprising 320 unique immune genesymbols). FIG. 1A shows the 21 of 326 CRCs selected by the 11 highestand 10 lowest values of the mean score of metagene #1.

Example 2. Pathologic and Clinical Findings

The patients with 21 CRCs selected by the 11 highest and 10 lowestvalues of the mean score of metagene #1 had a median age of 69 years(range, 51-83). Thirteen were male and seven were female. Most of theprimary CRCs were moderately differentiated (n=16); of these 8 weremetagene #1 “high” and 8 were metagene #1 “low”. Three tumors were welldifferentiated; of these one was metagene #1 “high” and two weremetagene #1 “low”. One CRC was poorly differentiated and it was metagene#1 “high”. Thus, there was no definable correlation between CRCsselected by metagene #1 and their grade of differentiation.

The relationship between overall survival of the patients and the immuneresponse as quantified by the score of metagene #1 for the 21 selectedCRCs was also examined. FIG. 1B shows the bar plot of these two groupsof metagene #1 selected CRCs: those with overall survival time less than2 years (shown on the left), and those with overall survival timegreater than 2 years (shown on the right). Score for metagene #1 isplotted on the Y-axis. As can be seen, there was a significant trend ofincreased overall survival (>2 years) of patients with CRCs with thehighest values of the mean score of metagene #1.

Example 3. Histopathologic and Immunohistochemical Findings

Immunohistochemistry and analysis were performed as follows. The tissueswere stained using the avidin-biotin-complex method with retrieval underhigh pH. Prediluted, monoclonal antibodies (mAb) to CD3 (rabbit mAb,Ventana Medical Systems, Inc., Tucson, Ariz.), CD20 (mouse mAb,Ventana), CD79a (mouse mAb, Ventana), Ki-67 (rabbit mAb, Ventana), andCD21 (mouse mAb, Novocastra Laboratories Ltd., Newcastle upon Tyne, UK)were used for the analysis of lymphoid infiltrates. The slides werede-paraffinized by heating at 56° C. for 30 minutes and by three washes,five minutes each, with xylene. Tissues were rehydrated by a series offive-minute washes in 100%, 95%, and distilled water. After blockingwith universal blocking serum (Ventana) for 30 minutes, the samples wereincubated with each primary mAb at 37° C. for 32 minutes. The sampleswere then incubated with biotin-labeled secondary mAb andstreptavidin-horseradish peroxidase for 30 minutes each. The slides weredeveloped with 3,3′-diaminobenzidine tetrahydrochloride substrate(Ventana) and counterstained with hematoxylin and bluing (Ventana). Thetissue samples were dehydrated and coverslipped. Appropriate cellconditioning (following the Ventana recommendations) was used forantigen retrieval for all antibodies. A negative control was included ofnon immune mouse sera and omitting the primary antibody during theprimary antibody incubation step. The positive controls were selectedfollowing the Ventana recommendations for CD3, CD20, CD79a, and Ki-67,and the Novocastra recommendations for CD21.

The mAb stained tissue slides were blinded and examined by twoindependent pathologists simultaneously. In case of discrepancy aconsensus was reached by the re-valuation of the slides. The positivityof the stains was calculated semi-quantitatively by estimating thepercent of nuclear positivity in the lymphoid cells.

Microscopically, all 10 of the lowest metagene #1-scored CRCs revealed aminimally dispersed or absent lymphocytic peritumoral host response, andlow to no appreciable expression for lymphocytic markers (FIGS. 2A, B).Conversely, all 11 of the highest metagene #1-scored CRCs, revealed amarked peritumoral lymphocytic host response organized, remarkably, asectopic lymph node-like structures by hematoxylin and eosin staining(FIG. 2C) and by immunohistochemistry (FIGS. 2D-I), particularly at theinvasive edge of the tumors. Of note, there was no statisticallysignificant difference between the presence of ectopic lymph node-likestructures and gender (p>0.5), tumor grade (p>0.5), tumor site location(p>0.5; Table 2), and tumor stage (p>0.5; Table 2).

Ectopic lymph node-like structures were observed intratumorally as well,occasionally accompanied by a lightly diffuse pattern of lymphocyteswithin the tumor parenchyma. The lymphoid structures were found tocontain follicles. The majority of the CD3⁺ T cells were located inparafollicular cortex-like zones (FIGS. 2D, H, I); scattered CD3⁺ Tcells were occasionally seen within these tumors as well. CD20⁺ B cellswere present almost exclusively within the follicular structures (FIGS.2E, F). In every case, CD79a⁺ B cell precursors were identified withinthe lymphoid follicles (FIG. 2G). CD21⁺ dendritic cells were presentwithin the follicular germinal centers, establishing the true follicularnature of the lymphoid aggregates (FIG. 2J). The Ki-67 expression wasfound only in the highest metagene #1-scored CRC cases, suggesting thatthese germinal centers are in different stage of maturation. Lymphocyticproliferation was found to contain both B and T lymphocytes, whichsuggests newly formed and/or activated ectopic lymph node-likestructures. Together, these findings support the hypothesis that thesefollicles represent secondary and/or tertiary ectopic lymph node-likestructures.

Example 4. Identification of a Chemokine Gene Signature

326 colorectal tumors and 12 genes comprising metagene #1 were sorted bythe metagene score (mean of probesets that map to a given 12-gene, geneset). Again, the 11 samples with the highest metagene score (shown onthe previous Figures) are shown at the top, and 10 samples with thelowest metagene score (shown on the previous Figures) are shown at thebottom. Table 1 shows the hierarchical clustering of 326 CRC tumors andthe selected 12 chemokines (i.e., CCL2, CCL3, CCL4, CCL5, CCL8, CCL18,CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13) that were mostcorrelated with the metagene #1 score. For each chemokine, the labelprovides gene symbol, numerical order on the corresponding probeset whenprocessed with APT package, as well as probeset identification.Collectively, these data demonstrate that the chemokines, because oftheir potent, biologic attraction of immune cell subtypes track stronglywith the formation or presence of ectopic lymph node-like structures inCRC tumor masses.

TABLE 1 Chemokine Gene Expression in Human Colon Tumors: A 12 GeneSignature Predicts Presence of Ectopic Lymph Nodes * Colon500, ChemokineCorrelation to Metagene#1 probe# probeset symbol^(†) rho p-value 651405_i_at CCL5 83% 0E+00 17867 204655_at CCL5 83% 0E+00 520451555759_a_at CCL5 81% 0E+00 17288 205242_at CXCL13 72% 0E+00 18402204103_at CCL4 72% 0E+00 18665 203915_at CXCL9 72% 0E+00 18007 204533_atCXCL10 66% 7E−42 12512 210072_at CCL19 61% 4E−34 235 32128_at CCL18 58%2E−30 8658 214038_at CCL8 58% 1E−30 12636 209924_at CCL18 58% 8E−31 6122216598_s_at CCL2 57% 4E−30 17425 205114_s_at CCL3 56% 2E−28 11482211122_s_at CXCL11 55% 1E−27 12419 210163_at CXCL11 55% 1E−27 17905204606_at CCL21 53% 5E−25 Table 1. * Hierarchical clustering of 326 CRCsand selected ^(†)12 chemokines (CCL2, CCL3, CCL4, CCL5, CCL8, CCL18,CCL19, CCL21, CXCL9, CXCL10, CXCL11, CXCL13) that are most correlatedwith metagene #1 score. Tumors are sorted by the metagene score,chemokines are clustered using Pearson correlation distance metric andWard linkage. For each chemokine, the label provides gene symbol,numerical order on the corresponding probeset when processed with APTpackage, as well as probeset id. Rho, Pearson correlation coefficient.

Table 1. *Hierarchical clustering of 326 CRCs and selected ^(†)12chemokines (CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9,CXCL10, CXCL11, CXCL13) that are most correlated with metagene #1 score.Tumors are sorted by the metagene score, chemokines are clustered usingPearson correlation distance metric and Ward linkage. For eachchemokine, the label provides gene symbol, numerical order on thecorresponding probeset when processed with APT package, as well asprobeset id. Rho, Pearson correlation coefficient.

There was a strong correlation of association between the chemokine geneprofile and the presence of ectopic lymph node-like structures in CRCs(See FIG. 3A). Hierarchical clustering of tumors with and withoutlymphoid structures was performed on a selected set of known chemokinegenes (shown in FIG. 3B). Strong correspondence exists between scoremean cutoffs for metagene #1 and the chemokine gene set. For each gene,a single representative probe set with the highest dynamic range acrossall profiled samples was picked from all probe sets that mapped to agiven gene symbol. Genes are clustered using Pearson correlationdistance metric; tumors are sorted by gene scores (shown in in FIG. 3B).

Example 5. Molecular Signatures and Clinical Parameters

Table 2 shows that metagene #1 and the 12-chemokine gene signature thatidentify the presence of ectopic lymph node-like structures in CRCs areindependent of tumor staging (TNM and Dukes), tumor site location, andtreatment received (e.g., surgery alone, surgery plus chemotherapy withor without external beam radiation).

TABLE 2 Molecular Signatures that Identify the Presence of Ectopic LymphNode Structures are Independent of Tumor Site Location, Stage andPatient Treatment Ectopic Lymph Tumor Dukes/TNM Signatures* Tumor^(†)Nodes Site Stage Treatment + T2151 + Rectum D/IVA 5FU + T4555 + LeftB/IIA None + T252 + Right C/IIIC 5FU + radiation + T2638 + Rectum C/IIIB5FU/Irinotecan + T4948 + Sigmoid C/IIIB 5FU + radiation + T1923 + LeftB/IIA 5FU + T462 + Rectum B/IIA None + T568 + Left NA^(‡)5FU/mitomycin/Irinotecan + radiation + T3701 + Right C/IIIB 5FU +T3395 + Sigmoid B/IIB None − T5108 − Right NA^(‡) 5FU − T2648 − Rectum—/0 None − T5029 − Sigmoid C/IIIB NA − T3138 − Left NA^(‡)5FU/Irinotecan − T6190 − Sigmoid D/IVA 5FU/Irinotecan/Erbitux/Irinotecan− T2588 − Left B/IIC 5FU/Irinotecan/5- FUDR + mitomycin/Irinotecan +radiation − T2157A1/A3 − Right D/IVA None − T4376 − Left B/IIC5FU/Irinotecan − T5162 − Left B/IIC 5FU + radiation − T2412 − LeftC/IIIB 5FU/Irinotecan/Erbitux Table 2. Molecular Signatures areIndependent of Tumor Stage and Patient Treatment. *Metagene #1 and12-chemokine signature. ^(†)CRCs that have the 10 highest and 10 lowestmetagene #1 and 12-chemokine signature scores. ^(‡)Not available.

The relationship between overall survival of the patients and metagene#1 and the 12-chemokine gene signature was also examined for the 21selected CRCs. FIG. 4B shows the bar plots of these groups ofsignature-selected CRCs: those with overall survival time less than 2years (shown on the left), and those with overall survival time greaterthan 2 years (shown on the right). Score for genes is plotted on theY-axis. As can be seen, there was a significant trend of increasedoverall survival (>2 years) of patients with CRCs with the highestvalues of the mean score of the 12-chemokine gene signature.

Example 6. Identification of T Cell Activation-Related Gene Signatures

The heatmaps of FIGS. 5A and 6A show strong correlations of associationbetween cytotoxic cell (lysosomal/proteolytic enzymes, granzymes,FcgR2s) and dendritic cell (S100 family, CD209) gene profiles,respectively, and the presence of ectopic lymph node-like structures inCRCs. Hierarchical clustering of tumors with and without ectopic lymphnode-like structures were then performed on a selected set of knowncytotoxic- and dendritic cell-related genes (shown in FIGS. 5B and 6B).Strong correspondence exists between score mean cutoffs for metagene #1and cytotoxic and dendritic cell-related gene sets. For each gene, asingle representative probe set with the highest dynamic range acrossall profiled samples was picked up from all probe sets that mapped to agiven gene symbol. Genes are clustered using Pearson correlationdistance metric; tumors are sorted by the genes score (shown in FIGS. 5and 6) and are computed as the mean value for each tumor across allgenes shown. Collectively, the cytotoxic and dendritic cell genesreflect an important immune cell composition of ectopic lymph node-likestructures in CRC.

FIGS. 7A-B show the bar plots of these groups of signature-selectedCRCs: those with overall survival time less than 2 years (shown on theleft), and those with overall survival time greater than 2 years (shownon the right). Score for genes is plotted on the Y-axis. As can be seen,there was a significant trend of increased overall survival (>2 years)of patients with CRCs with the highest values of the mean score of thecytotoxic and dendritic cell gene signatures.

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Other Embodiments

It is to be understood that while the invention has been described inconjunction with the detailed description thereof, the foregoingdescription is intended to illustrate and not limit the scope of theinvention, which is defined by the scope of the appended claims. Otheraspects, advantages, and modifications are within the scope of thefollowing claims.

What is claimed is:
 1. A method of treating a subject who has a solidtumor, the method comprising: obtaining cells from the tumor;determining gene expression levels of chemokine (C-C motif) ligand 2(CCL2), CCL3, CCL4, CCL5, CCL8, chemokine (C-C motif) ligand 18(pulmonary and activation-regulated) (CCL18), CCL19, CCL21, chemokine(C-X-C motif) ligand 9 (CXCL9), CXCL10, CXCL11, and CXCL13 in the tumorcells; comparing the tumor gene expression levels to reference geneexpression levels; identifying a subject who has tumor gene expressionlevels above the reference gene expression levels; and administering animmunotherapy treatment for cancer to a subject who has tumor geneexpression levels above the reference gene expression levels.
 2. Themethod of claim 1, wherein determining gene expression levels comprisesdetermining protein levels or mRNA levels.
 3. The method of claim 2,wherein determining mRNA levels comprises using reverse transcriptionpolymerase chain reaction or a gene chip.
 4. The method of claim 1,wherein the subject is a human.
 5. The method of claim 1, wherein thetumor is non small cell lung cancer.
 6. The method of claim 1, whereinthe treatment comprises administering cyclophosphamide;anti-interleukin-2R immunotoxin; and/or an anti-cancer antibody selectedfrom the group consisting of anti-CD137, anti-programmed death-1receptor (PD1), and anti-cytotoxic T-lymphocyte antigen-4 (CTLA-4). 7.The method of claim 6, wherein the antibody is anti-CD137.
 8. The methodof claim 6, wherein the antibody is anti-PD1.
 9. The method of claim 6,wherein the antibody is anti-CTLA-4.